Two processes are involved in manipulating or transforming an array of data such as a television video image which has been stored in a frame store. One process is that of addressing or moving the data values which represent the image at source image sampling points from their initial location in the source image to a transformed location in a target image. The other process is filtering.
Filtering is required to deal with data points within the transformed target image which fall between sampling points of the source image. That is, the target image must be structured as an array of target image sampled data points, each of which is derived from transformed source image data points which may fall between target image data points. In addition, if the size of the image is to be changed the source image must be low-pass filtered so that the transformed image will have a maximum frequency less than half the predetermined sampling frequency of the target image in order to satisfy the Nyquist sampling criteria. This is particularly true for image compression and failure to do so results in problems such as aliasing.
Frame stores are frequently used in video processing systems and store a video image as an array of pixels or digital values representing sampled data points. If a video image is received from an analog source such as a camera, it must first be periodically sampled and digitized and then written into a frame store as an array of pixels or data point values. If a stored image is to be transformed, as by compression, magnification, or translation most prior art systems pass a fixed length transversal filter over the data points in the source frame store prior to transformation. By using selected filter parameters, the cutoff frequency of the filter is varied to produce a progressively lower cutoff frequency as the amount of image compression increases. This effectively maintains the frequency of the target data below one-half the target data sampling frequency after compression. Oftentimes it is desired to present to a viewer an image which gradually decreases in size. However, during a gradual increase in the compression ratio of successive frames, new sets of filter parameters must be switched into the filter from time-to-time as the target image becomes progressively smaller. Each change of filter parameters results in an esthetically displeasing sudden change in the frequency response of the viewed target image. In addition, a very low cutoff frequency is required for highly compressed images. Because of cost, practical filters are limited to ten or fewer taps or sampling points. As image size decreases this translates into fewer and fewer sampling points in the transformed target image. As the target picture size falls below the minimum size that can be supported by the filter severe aliasing problems appear and picture quality rapidly deteriorates.
Known filter design theory holds that one should start by designing a hypothetical continuous reference filter with a cutoff frequency of one-half the sample frequency of the system, i.e. the sample frequency of the source image. This reference filter is then time-scaled by the inverse of the anticipated compression factor to obtain the proper filter for use in the source space. In other words the impulse response of the reference filter is "stretched" so that when compression occurs the original impulse response is recovered. The filter coefficients for a digital filter may be derived from this imaginary stretched continuous filter by sampling its impulse response at points coincident with a grid of source image sample points.
As the reference impulse response stretches in source space, it spans more and more grid points, requiring the filter to have many coefficients. For example, if the full-size image reference response lasted eight sample points, a 10:1 compression would require an 80-point filter and a 20:1 compression would require a 160-point filter. This would be economically impractical. Similarly, a full size image reference response that lasted four sample points would require 40-point and 80-point filters for compressions of 10:1 and 20:1 respectively.
Another technique for accommodating this problem is referred to as predecimation and is disclosed in application Ser. No. 310,907 (abandoned), filed Sept. 28, 1981 by Gabriel and Evans for "System for Spatially Transforming Images". The predecimation technique requires the successive generation and storage of multiple half-size copies of each line of a video image. Reduction to the required target size is then accomplished by selecting the smallest predecimated copy which is larger than the target size and providing the additional compression required to reach the final target size. This predecimation technique uses economically practical, highquality filters since the compression for any single filter pass is never greater than 2:1. However, in order to operate at real-time video rates predecimation requires expensive high-speed circuits connected for parallel operation. Furthermore, there is a visible discontinuity in the frequency response of the target image during a continuous change in the compression ratio as the system switches from one predecimated copy to the next predecimated copy.
In the present example a target image-referenced filtering system is illustrated in the context of a digital electronic still store for television pictures. A similar system using source image-referenced filtering is shown in U.S. Pat. No. 4,302,776 to Taylor et al, entitled "Digital Still Picture Storage System With Size Change Facility".